{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt\n",
    "from torchvision import datasets,transforms\n",
    "import torch.optim as optim"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-17T00:50:39.920637800Z",
     "start_time": "2023-11-17T00:50:07.209828Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "True"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.is_available()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-17T00:50:51.403455400Z",
     "start_time": "2023-11-17T00:50:49.992064100Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 导入模型"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet101_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet101_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNet(\n",
      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (relu): ReLU(inplace=True)\n",
      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "  (layer1): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer2): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer3): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (4): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (5): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (6): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (7): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (8): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (9): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (10): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (11): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (12): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (13): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (14): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (15): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (16): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (17): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (18): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (19): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (20): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (21): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (22): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer4): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "resnet = torchvision.models.resnet101(pretrained=True)\n",
    "print(resnet)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-17T00:53:14.741927100Z",
     "start_time": "2023-11-17T00:53:10.397513400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "for p in resnet.parameters():\n",
    "    p.requires_grad=False\n",
    "resnet.fc=nn.Linear(2048,2,bias=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-17T00:56:27.683386Z",
     "start_time": "2023-11-17T00:56:27.663390300Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<P>过深的网络会引起梯度消失，会导致损失值收敛过慢从而影响精度.为了解决该问题,微软团队提出了ResNet模型,成功解决了上述难题.ResNet主要使用了跳跃连接的方法，可以成功训练高达152层的网络。跳跃连接的具体做法是在两层或两层以上的节点两端添加了一条捷径，这样一来原来的输出F(X)就变成了F(X)+x.跳跃连接能够解决梯度消失的原因在于:它能够在更底层的参数层(权重层)与输出层之间建立一条通道。当进行反向传播时,损失值能够直接接触到底层的参数</P>"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 导入数据集"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "(55, 14)"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = 'F:/图像识别数据集/archive'\n",
    "data_transform = {\n",
    "    'train':transforms.Compose([\n",
    "        transforms.Resize(230),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))\n",
    "    ]),\n",
    "    'val':transforms.Compose([\n",
    "        transforms.Resize(230),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))\n",
    "    ])\n",
    "}\n",
    "import os\n",
    "\n",
    "trainset = datasets.ImageFolder(os.path.join(data_path,'train'),transform=data_transform['train'])\n",
    "testset = datasets.ImageFolder(os.path.join(data_path,'val'),transform=data_transform['val'])\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(trainset,batch_size=5,shuffle=True,num_workers=4)\n",
    "test_loader = torch.utils.data.DataLoader(testset,batch_size=5,shuffle=True,num_workers=4)\n",
    "len(train_loader),len(test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-17T01:31:43.822462900Z",
     "start_time": "2023-11-17T01:31:43.454386200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "def imshow(inputs):\n",
    "    inputs = inputs/2+0.5\n",
    "    inputs = inputs.numpy().transpose(1,2,0)\n",
    "    plt.imshow(inputs)\n",
    "    plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-17T01:38:00.454305Z",
     "start_time": "2023-11-17T01:38:00.383342500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cat\n",
      "cat\n",
      "dog\n",
      "dog\n",
      "dog\n"
     ]
    }
   ],
   "source": [
    "imgs,labels = next(iter(test_loader))\n",
    "imshow(torchvision.utils.make_grid(imgs))\n",
    "class_names = ['cat','dog']\n",
    "for i in labels.numpy():\n",
    "    print(class_names[i])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-17T01:40:13.202706300Z",
     "start_time": "2023-11-17T01:40:01.641692900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "## 训练函数\n",
    "import datetime\n",
    "def train_loop(model,loss_fn,optimizer,train_loader,n_epochs):\n",
    "    for epoch in range(1,n_epochs+1):\n",
    "        loss_train =0.0\n",
    "        for i,data in enumerate(train_loader,1):\n",
    "            imgs,labels =data\n",
    "            imgs,labels = imgs.cuda(),labels.cuda()\n",
    "            outputs = model(imgs)\n",
    "            loss = loss_fn(outputs,labels)\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            loss_train+=loss.item()\n",
    "            if i%5==0:\n",
    "                print('{}, Epoch:{}, i:{}, 训练损失:{}'.format(datetime.datetime.now(),epoch,i,loss_train/25))\n",
    "                loss_train = 0.0"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-17T01:57:40.590813400Z",
     "start_time": "2023-11-17T01:57:40.566769700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-11-17 10:01:13.553693, Epoch:1, i:5, 训练损失:0.14942707300186156\n",
      "2023-11-17 10:01:14.097751, Epoch:1, i:10, 训练损失:0.1404765009880066\n",
      "2023-11-17 10:01:14.642694, Epoch:1, i:15, 训练损失:0.110130934715271\n",
      "2023-11-17 10:01:15.190694, Epoch:1, i:20, 训练损失:0.0774744427204132\n",
      "2023-11-17 10:01:15.735736, Epoch:1, i:25, 训练损失:0.04942658841609955\n",
      "2023-11-17 10:01:16.283709, Epoch:1, i:30, 训练损失:0.05881772696971893\n",
      "2023-11-17 10:01:16.828738, Epoch:1, i:35, 训练损失:0.0927759838104248\n",
      "2023-11-17 10:01:17.380693, Epoch:1, i:40, 训练损失:0.17718981564044953\n",
      "2023-11-17 10:01:18.564119, Epoch:1, i:45, 训练损失:0.07997205406427384\n",
      "2023-11-17 10:01:19.156091, Epoch:1, i:50, 训练损失:0.09476083934307099\n",
      "2023-11-17 10:01:19.701095, Epoch:1, i:55, 训练损失:0.033445101007819174\n",
      "2023-11-17 10:01:25.691019, Epoch:2, i:5, 训练损失:0.10025171272456646\n",
      "2023-11-17 10:01:26.238017, Epoch:2, i:10, 训练损失:0.02500853657722473\n",
      "2023-11-17 10:01:26.787046, Epoch:2, i:15, 训练损失:0.08208867251873016\n",
      "2023-11-17 10:01:27.337017, Epoch:2, i:20, 训练损失:0.02025989383459091\n",
      "2023-11-17 10:01:27.883019, Epoch:2, i:25, 训练损失:0.09597097672522067\n",
      "2023-11-17 10:01:28.427055, Epoch:2, i:30, 训练损失:0.03189089767634869\n",
      "2023-11-17 10:01:28.970018, Epoch:2, i:35, 训练损失:0.04457547198981047\n",
      "2023-11-17 10:01:29.513018, Epoch:2, i:40, 训练损失:0.02837670847773552\n",
      "2023-11-17 10:01:30.058023, Epoch:2, i:45, 训练损失:0.12174668103456497\n",
      "2023-11-17 10:01:30.600018, Epoch:2, i:50, 训练损失:0.03409801796078682\n",
      "2023-11-17 10:01:31.140017, Epoch:2, i:55, 训练损失:0.029099556505680083\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(resnet.parameters(),lr=0.001,momentum=0.9)\n",
    "loss_fn =  nn.CrossEntropyLoss()\n",
    "train_loop(resnet.cuda(),loss_fn=loss_fn,optimizer=optimizer,train_loader=train_loader,n_epochs=2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-17T02:01:31.897019700Z",
     "start_time": "2023-11-17T02:00:16.634002900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "def test_loop(model,test_loader):\n",
    "    correct=0\n",
    "    total =0\n",
    "    with torch.no_grad():\n",
    "        for imgs,labels in test_loader:\n",
    "            imgs,labels = imgs.cuda(),labels.cuda()\n",
    "            outputs = model(imgs)\n",
    "            _,preds = torch.max(outputs,dim=1)\n",
    "            correct+=int((preds==labels).sum())\n",
    "            total+=labels.shape[0]\n",
    "    print('精度:{:.3f}%'.format(correct/total*100))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-17T02:08:51.878328800Z",
     "start_time": "2023-11-17T02:08:51.819024900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "精度:98.571%\n"
     ]
    }
   ],
   "source": [
    "test_loop(resnet.cuda().eval(),test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-17T02:09:15.152500700Z",
     "start_time": "2023-11-17T02:09:06.734691300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "真实值---预测值\n",
      "cat --- cat\n",
      "cat --- cat\n",
      "dog --- dog\n",
      "dog --- dog\n",
      "dog --- dog\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "outputs = resnet.cuda().eval()(imgs.cuda())\n",
    "_,preds = torch.max(outputs,dim=1)\n",
    "print('真实值---预测值')\n",
    "for i,j in zip(labels,preds):\n",
    "    print(class_names[i],'---',class_names[j])\n",
    "imshow(torchvision.utils.make_grid(imgs))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-17T02:14:09.312190700Z",
     "start_time": "2023-11-17T02:14:08.711813200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0, 0, 1, 1, 1])\n",
      "tensor([0, 0, 1, 1, 1], device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "print(labels)\n",
    "print(preds)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-17T02:14:41.188794100Z",
     "start_time": "2023-11-17T02:14:40.994794300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
